Income in Jeopardy: How Losing Employment Affects the

Income in Jeopardy: How Losing Employment Affects the

Income in Jeopardy: How Losing Employment Affects
the Willingness to Take Risks
Clemens Hetschko
Malte Preuss
School of Business & Economics
Discussion Paper
Economics
2015/32
Income in Jeopardy:
How Losing Employment Affects the Willingness to Take Risks
Clemens Hetschko, Malte Preuss*
Freie Universität Berlin
December 15, 2015
Abstract
Using German panel data, we assess the causal effect of job loss, and thus of an
extensive income shock, on risk attitude. In line with predictions of expected utility
reasoning about absolute risk aversion, losing one’s job reduces the willingness to take
risks. This effect strengthens in previous hourly wage, begins to manifest itself as soon
as an employee perceives the threat of job loss and is of a transitory nature. The change
in stated risk attitude matches observable job finding behaviour, confirming the
behavioural validity of our results.
JEL Classification: D81; J64; J65
Keywords: absolute risk aversion; income shock; job loss; plant closure; general risk attitude
*
Corresponding author. Freie Universität Berlin, School of Business and Economics, Boltzmannstraße 20, D14195 Berlin, E-mail: malte.preuss@fu-berlin.de.
Acknowledgements
We are grateful for comments made by Ronnie Schöb, Daniel Nachtigall and seminar participants at the University
of Potsdam (2015), the SOEP group (Berlin, 2015) and the Max Planck Institute for Tax Law and Public Finance
(Munich, 2015). We also thank participants of the Trier Workshop in Economics (2015), the 5th Workshop in
Labour Economics and Social Policy (Dresden, 2015), the 1st BeNA Labour Economics Workshop (2015), the 1st
PhD Conference in Behavioural Science (Stirling, 2015), the IAREP/ SABE Joint Conference (Sibiu, 2015) and
the 5th PhD Workshop in Empirical Economics (Potsdam, 2015)
-1-
1.
Introduction
The willingness to take risks strongly affects economically important outcomes such as
entrepreneurial activity, migration and households’ allocation of financial assets. Some part of
individual risk attitude is rooted in genetic dispositions, socialisation and personality
development. 1 Beyond that, life experiences such as poverty (e.g. Haushofer and Fehr 2014),
child birth (Görlitz and Tamm 2015) or being exposed to violence (Callen et al. 2014) shape
people’s willingness to take risks. The Great Depression (Malmendier and Nagel 2011) and
natural disasters have been shown to increase risk aversion, probably because the appearance
of a rare event amplifies its general perception (Cameron and Shah 2015, Goebel et al. 2015).
We analyse the risk-taking effect of another source of substantial individual risk concerning the
vast majority of employees in market economies: losing one’s job. As approaching and
experiencing job loss places, first and foremost, workers’ current and future income in jeopardy,
this event facilitates a natural experiment for studying the impact of an extensive income shocks
on risk attitude.
Previous research does not come up with clear answers on how a negative income shock
may alter individual risk attitude. Brunnermeier and Nagel (2008) find that fluctuations in
household wealth do not yield any adjustment in risk-taking with respect to households’ asset
allocations, probably because of inertia. Post et al. (2008) analyse repeated decisions of people
gambling in the TV show ‘Deal or No Deal?’ and conclude that risk aversion increases after
both belied and excelled expectations. Both Brunnermeier and Nagel (2008) as well as Post et
al. (2008) use data on choices to reveal risk attitude. In principle, other dominant factors (like
inertia) related to these choices may veil the direct impact of a financial gain or loss on general
risk attitude, which potentially affects all individual choices. In contrast, we provide evidence
on the effect of an income shock on a direct measure of risk attitude.
To the best of our knowledge, Sahm (2012) provides the only existing study on the impact
of job loss on risk attitudes. Besides various other insights pointing to time-invariant risktaking, she does not find that elder workers in the US change risk attitude in the wake of being
dismissed. In contrast to her, we focus on job losses due to the closure of a complete plant or
firm and thus on a much more specific type of dismissal. This is for two reasons: First, many
dismissals may often be preventable by the worker (dismissal due to misconduct or shirking)
and therefore result from given risk attitude rather than causing a change in risk attitude.
1
See Cesarini et al. (2010), Mata et al. (2012) as well as Harrati (2014) on genetic disposition. See Dohmen et al.
(2012) as well as the review of Becker et al. (2012) on sozialisation and personality development.
2
Second, the impact of dismissals for any reason may not generalise to the average risk-taking
effect of job loss since the affected subgroup of workers (low-skilled, health problems) does
not represent the workforce well.
To the extent that workers cannot insure the income risk associated with non-controllable
job loss, its impact on risk attitude will be that of a background risk. Public insurances replace
wage income only to some extent, and then only for a limited period of time. They do not
account for the loss of company pensions, the scarring effects of unemployment (reduced
earnings when reemployed, e.g. Arulampalam et al. 2001) and the loss of non-monetary welfare
(e.g. reductions of social participation and identity utility, see Kunze and Suppa 2014, Hetschko
et al. 2014). As a result, we argue that increasing risk of job loss will cause workers to avoid
other controllable risks more often as decreasing absolute risk aversion (DARA) characterises
their utility function. 2
To test this notion by estimating the causal effect of loss of work on risk attitude, we apply
a difference-in-differences approach based on German Socio-economic Panel data (SOEP). We
assign workers who experience job loss due to the closure of the complete plant or company to
the treatment group and similar employees who do not lose their jobs to a control group. 3 As a
behaviourally valid measure of general risk attitude, we use the stated willingness to take risks.
It turns out that exogenous job loss indeed decreases the willingness to take risks. The effect
already begins to manifest itself before the job loss event ultimately occurs, as workers may
perceive that employment is increasingly at risk. Pre-treatment hourly wage as proxy for the
losses of earnings and nonwage benefits associated with job loss amplifies the negative impact
of job loss on risk-taking. This confirms that the losses of current income and the fear of losing
future income are driving forces behind the impact of job loss on risk-taking. In the aftermath
of the event, the willingness to take risks gradually returns to its initial level as workers become
reemployed. This suggests that the risk attitude effect of losing work is of a transitory nature.
The appearance of job loss does not seem to change the future perception of this specific risk
or of other calamities. Additional empirical analyses point to the behavioural validity and
economic significance of our findings.
Our findings not only complement the literature on the origins of risk-taking, they also
concern the theoretical foundation of the increasingly popular general risk attitude measure we
2 DARA is not only an intuitive, but also an often empirically proven assumption (see, e.g., Bombardini and Trebbi
(2012) as well as Guiso and Paiella (2008).
We hereby follow, inter alia, Kassenboehmer and Haisken-DeNew (2009), Schmitz (2011), Marcus (2013).
3
3
use. 4 Job loss affects the willingness to take risks in the way predicted for absolute risk aversion.
We therefore recommend using the survey item accordingly. While our study shows that this
measure responds to certain life events, it should not be implied that general doubts about the
theoretical assumption of time-invariant general risk preferences, which reflect the shape of the
utility function, are justified. Empirical researchers should, however, be aware of the fact that
the general risk attitude measure is not exogenous to living conditions and life experiences. In
consequence, if the willingness to take risks is examined regarding its effect on any outcome,
researchers must consider simultaneity bias.
We proceed as follows. Section 2 applies expected utility reasoning to derive theoretical
hypotheses about the impact of job loss on risk attitude. In Sections 3 and 4, we describe our
identification strategy, data and sampling. Section 5 documents the results of our empirical
analyses and finally Section 6 concludes and discusses our findings.
2.
Theoretical background and hypotheses
Consider an individual i with a von Neumann-Morgenstern utility function u ( wi , yi ) that
describes utility received from future ‘labour income’ wi, and a non-insurable income loss yi .
Both are defined broadly and can consist of wage and nonwage job characteristics. wi results
from expectations concerning the benefits of working, adjusted by any risk they can influence.
In contrast, job loss will concern yi when employees can neither influence its probability, such
as in the case of plant closure, nor insure themselves comprehensively. As argued in the
introduction, a significant part of the individual welfare loss associated with job loss may be
non-insurable. We therefore consider exogenous job loss hereinafter as immutable and noninsurable background risk.
Two elements shape the risk of job loss. Its probability JL~ P( JL | ωt ) , which depends on the
set of information ωt pointing to job loss at a point in time t and the total damage vi resulting
from job loss. Abstracting from any other immutable risk, the total expected loss from job loss
yi P( JL | ωt ) ⋅ vi . The set of information changes when new information about a job loss
is=
1 with ωt=−2 as
arrive. If job loss becomes absolutely certain, P( JL | ωt =
−2 ) < P ( JL | ωt =
0) =
information set way before the actual job loss takes place and ωt=0 as set of information right
after the job loss has occurred. To assess the influence of job loss on risk attitude, we measure
4 For primarily methodological discussions of the survey item see Dohmen et al. (2011) and Charness et al. (2013).
For recent applications of the item see, for instance, Pannenberg (2010), Jaeger et al. (2010), Dohmen et al. 2012,
2015a, 2015b, Brachert and Hyll (2014), Skriabikova et al. (2014), Fossen and Glocker (2014), Görlitz and Tamm
(2015), Schurer (2015), Goebel et al. (2015).
4
its theoretical effect on absolute risk aversion in the spirit of Arrow and Pratt. Following
Kihlstrom et al. (1981) and Nachman (1982), the presence of a background risk renders
Eu ''( wi − yi )
ARA( wi − yi ) =
−
.
Eu '( wi − yi )
In consequence, the shift in ωt increases the expected loss from job loss for the next considered
period. By assuming u ( wi , yi ) to imply DARA, an increase in immutable risk raises the level of
absolute risk aversion (Pratt and Zeckhauser 1987, Kimball 1993, Eeckhoudt et al. 1996).
Hypothesis 1: Job loss increases the level of ARA.
Workers may anticipate job loss from a certain point in time onwards. Lacking competiveness
of the firm, rumours, mass layoffs, or, at the very end, insolvency proceedings may trigger, step
by step, an update in the information set. The shift in ωt is therefore not a strict binary change,
but
a
gradual
or
iterative
process
till
t=0
is
reached,
i.e.
P( JL | ωt =
−2 ) < P ( JL | ωt =
−1 ) < P ( JL | ωt =
0 ) . Hence, the job loss probability is likely to grow slowly
with new arriving information, causing an early update in expectations and, therefore, in ARA,
too.
Hypothesis 2: ARA gradually increases before job loss occurs.
The more time has passed by after a job loss event, the more workers will be observed to be
reemployed. However, job search takes some time and a new job often starts with probation or
a fixed-term contract, keeping uncertainty about employment stability and thus uncertainty
about future incomes at a high level. In short, job loss is still increasing ARA compared to the
time before the event even when workers have recently started a new job. As time goes by,
however, employees establish themselves in the new job and gather new information about the
next involuntary job loss. The new set of information after re-entering employment and passing
the new job’s uncertainty ( ωt=1 ) should imply a similar job loss prospect as ωt=−2 does
( P( JL | ωt =
1 ) ) . Hence, the job loss probability shifts back and so does ARA.
−2 ) ≈ P ( JL | ωt =
Hypothesis 3: ARA gradually returns towards its initial level after job loss occurred.
In addition, we expect heterogeneous effects. As already indicated by the index i, labour market
income and expected loss differ between individuals due to individual resources. Workers who
have high levels of education, for instance, earn higher wages than less educated workers and
thus stand to lose much more, both in terms of future income and in terms of other benefits of
employment (e.g. status), in the case of job loss. In the following, we refer to those highly paid
5
employees as high-skilled workers (i = h) and to workers with relatively low wages are referred
to as low-skilled workers (i = l). The heterogeneity results in wh > wl and vh > vl . Assuming
the same change in the set of information for both types, the inequality in individual damage
∆yi
will yield ∆y h > ∆yl with =
( P( JL | ωt =0 ) − P( JL | ωt =−2 ) ) ⋅ vi ,
when an exogenous job loss
becomes more likely and finally occurs. Whether this leads to a bigger change in ARA for high
skilled individuals depends on the utility function and the inequality in expected income. By
assuming
DARA,
a
marginal
change
∂ ARA( wh − y h ) / ∂ ( w − y ) ≤ ∂ ARA( wl − yl ) / ∂ ( w − y )
if
in
income
wh − y h ≥ wl − yl
leads
to
holds. Hence,
∆y h > ∆yl resolves in a greater change in ARA for h-types only if three conditions are fulfilled:
First, the difference in income is sufficiently small. Second, ARA is only weakly convex, i.e.
∂ 2 ARA( wh − y h ) / ∂ ( w − y ) 2 is close to zero. And last, ∆y h − ∆yl is sufficiently big.
Hypothesis 4. Job loss changes ARA of high-skilled workers more than ARA of low-skilled workers.
3. Data
Our analysis is based on ten waves (2004-2013) of German Socio-economic Panel (SOEP) data
(Wagner et al. 2007). Each year, roughly 20,000 individuals living in 11,000 households
provide information about manifold personal perceptions and attitudes, their employment
status, income, health and much more. The time interval between two SOEP interviews is
approximately one year. Because of its panel structure and the opportunity to analyse
exogenously triggered job losses (plant closure) as well as the availability of a continuously
repeated question on the willingness to take risks, the SOEP stands out when compared with
other comparable representative panel data sets regarding the purposes of this study.
As an inverse measure of ARA, we use the following question on general risk attitudes
(GRA) that is measured in 2004, 2006 and each year from 2008 onwards:
Would you describe yourself as someone who tries to avoid risks (risk-averse) or
as someone who is willing to take risks (risk-prone)? Please answer on a scale from
0 to 10, where 0 means “risk-averse” and 10 means “risk-prone”.
According to the findings of Dohmen et al. (2011) as well as Fossen and Glocker (2014), people
answer in line with alternative measurements of risk attitudes (risk attitudes revealed through
decisions under uncertainty, such as real-stake lotteries, holding stocks, being self-employed,
educational choices). Thus, the item can be considered a behaviourally valid measure of risk
attitude, which we will discuss further with respect to our results in Section 5.5. As risk attitudes
seem to differ to some extent between areas of life, our measure may be even better suited to
6
ascertain risk attitude than hypothetical or actual lotteries and gambles, which confront
respondents with a very specific situation.
When workers have terminated an employment relationship between two SOEP interviews,
they are asked about the specific reason: ‘How did that job end?’. Answers that the ‘office or
place of work has closed’ (plant closure in the following) identify exogenously triggered job
losses best. At two points, we make also use of data about other dismissals (‘I was dismissed
by my employer’) to show how including more endogenous reasons for job loss would affect
our results. Further possible answers to the question on terminated employment are not
considered, namely a notice of resignation, mutual agreement with employer, the end of a
temporary contract, retirement or taking a leave of absence, maternity leave or parental leave.
The sample we analyse consists of initially ‘regular’ employees only. They are either
fulltime or part-time employed and spend more than 15 hours per week working, which is the
legal threshold between marginal employment and regular employment in Germany.
Observations of self-employed workers are not considered. 5 However, we do not restrict the
sample with respect to workers’ labour market activities after job loss has taken place. Besides
having taken up a new regular employment, they can be unemployed, have left the workforce,
or are doing anything else (e.g. occasional jobs). Not being selective at this point avoids any
systematic bias by sampling. Unemployment duration analyses will distinguish between
employment states after job loss (Section 5.5). Our sample is limited to workers who are older
than 20 years, but younger than 65 years. For our investigation period that is restricted by the
availability of the GRA measure, we can observe 37,700 observations of regular employees, of
which 239 experience job loss for the reason of plant closure.
Some individual characteristics are used to identify high-skilled individuals (named h-type
in Section 2) and low-skilled individuals (l-type) in order to test our fourth hypothesis. First,
we compute the individual hourly gross wage. It is based on information of actual weekly
working hours and gross monthly labour income. Second, we use two discrete measurements
of individual skills. Education is classified according to the ISCED-97 scale. l-types have no
more than secondary education (up to ISCED-97 level 3, which is the median level). h-types
are educated at least at ISCED-97 level 4 (anything beyond secondary education). As another
5
We do not exclude public sector employees although they are much less likely to experience plant closure than
private sector employees. However, excluding public sector would not yield results that are different from those
presented in the following.
7
alternative, we identify people as h-types that have above-median autonomy in occupational
actions, such as managers (below-median autonomy as l-types).
Beyond that, we utilise data on various further socio-demographic characteristics (age, net
household income adjusted by OECD equivalent weights, overall lifetime unemployment
experience to date in years, gender, marital status, children living in household, marital status,
migration background) and job characteristics (gross hourly wage in Euros, tenure in years,
level of occupational autonomy, company size, daily working hours, part-time employment,
public sector employment, sector of industry). Finally, we merge our data with precise
‘INKAR’ (indicators of the development of cities and regions) information about
unemployment rates of the 96 German planning regions (Raumordnungsregionen, see BBSR
2015).
4. Empirical identification
To test our hypotheses, we apply a difference-in-differences approach to identifying the effect
of exogenous job loss on the general willingness to take risks. The treatment group of regular
employees lose their jobs for the reason of plant closure between two SOEP interviews, which
we refer to as t = −1 and t = 0 in the following. Accordingly, we assume that plant closures
occur independent of workers’ characteristics. We discuss this assumption and provide
evidence for its validity in the course of our robustness analysis (Section 5.6). Job loss may
often bring about job insecurity in t = −1 which means that the treatment might affect the
willingness to take risks already at this point in time. Our pre-treatment reference point is hence
t = −2, which means that we assume that workers do not anticipate the plant closure event if it
takes place at least one year later. A control group of regular employees is included in order to
control, for instance, for time trends explaining changes in the willingness to take risks. This
group does not experience a job loss and stays employed at least for the duration of three SOEP
interviews in a row, which equal t = −2, t = −1 and t = 0.
We impute missing answers to the willingness to take risks question in t = −2 by the
previous interview (t = −3), if the individual is observed as employed in this specific period.
The setting requires that members of the treatment and the control group continue to participate
in the survey for at least three interviews in a row. Given this and all the other restrictions (such
as the availability of data on individual characteristics taken into account in the following
analyses), our sample includes 239 observations in the treatment group and 37,461 observations
in the control group.
8
The treatment effect that allows us to test our first hypothesis is the difference between
treatment and control group in the within-group change of the willingness to take risks between
t = −2 and t = 0. To be able to consider time effects, we employ a regression approach
explaining the change in general risk attitude (ΔGRA) between the two points in time, dependent
on the treatment (dummy Loss = 1, control group: Loss = 0) and the year of t = 0 (Y). We
include vectors of controls for pre-treatment (measured at t = −2) socio-demographic
characteristics (SD), job characteristics (JC) and parallel life events (Shocks between t = −2 and
t = 0) that account for non-random treatment and help us to approach the true average treatment
effect. The empirical model can be written as
(1)
∆GRAi = α + βLossi + γ ' SDi + δ ' JCi + σ ' Shocksi + θ ' Yi + εi
with α as the average change of the general risk aversion of the reference group and ui as the
error term. Consequently, sign and significance of the β-coefficient provide us with evidence
regarding Hypothesis 1. Subgroup analyses will clarify whether the corresponding regression
results vary by gender, age or employment status in t = 0.
Hypothesis 2 suggests decreasing GRA awhile for some time directly before job loss. We
therefore test whether a negative effect of Lossi appears when we estimate ΔGRA as the
difference between the two pre-treatment points in time t = −2 and t = −1. Regarding reversion
of the potential job loss effect on risk-taking (Hypothesis 3), we define the change in GRA
between t = −2 to t = 1 as dependent variable. In addition, we can test whether the willingness
to take risks of treatment and control group follow a common trend before the treatment takes
place by estimating ΔGRA between t = −3 to t = −2.
According to Hypothesis 4, an effect of job loss on the absolute risk aversion is supposed
to be driven by high-skilled workers, which we approximate by pre-treatment gross hourly
wage, education and level of educational autonomy (Section 3). To examine the role of these
characteristics, it is not sufficient to test whether adding them to (1) alters β. If Hypothesis 4
holds, a variation in skills plays a different role for the treatment group than for the control
group: high-skilled treated are exposed to a bigger income loss than low-skilled ones, when a
job loss arises. As discussed in Section 2, this implies a heterogeneity of the subsequent change
in GRA within this group, i.e. ∆GRA should vary by pre-treatment skills. In contrast, if no
additional threats arise the level of income risk should have no effect on ∆GRA. Therefore, the
level of skills will have a different impact on the change in GRA between treatment and control
group. We therefore need to estimate interaction effects of proxies for skills and Lossi in order
9
to examine whether skills matter to the risk attitude effect of losing work. The best
representation of individual productivity and, hence, future income and employment prospects,
may be one’s (pre-treatment) gross hourly wage (Wagei). We therefore modify (1) and estimate
∆GRAi = α + β1 Lossi + β2 ( Lossi × Wagei ) + β3Wagei
(2)
+ γ ' SDi + δ ' JCi + σ ' Shocksi + θ ' Yi + εi
In addition, we test interactions of the treatment dummy with education and autonomy in
occupational actions as further proxies for individual productivity.
5.
Results
5.1 Mean analyses
As a first step of our difference-in-differences analysis, we compare the average two-year
change from t = −2 to t = 0 in the general willingness to take risks between treatment and
control group (see Table 1). It turns out that job loss is indeed accompanied by a 0.4 point
stronger reduction in willingness to take risks than staying employed (p < 0.05). Moreover, the
descriptive figures do not imply a selection into the treatment by relatively risk-averse or
relatively risk-prone people as the pre-treatment level of GRA does not differ significantly
between the two groups.
The figures imply that the treatment is not completely random. Members of the control
group receive higher wages and have more household income available, can act more
autonomously in their firms, work in bigger firms, work less hours and are less likely to work
in the private sector than people who experience job loss about one to two years later. In
addition, job loss due to plant closure is more prevalent in some industries, such as services,
than in others. All further characteristics presented in Table 1 do not concern one of the two
groups more significantly than the other.
10
Table 1: Descriptive statistics
Scale
Number of observations:
Willingness to take risks
GRA (pre-treatment, mean)
0 - 10
Change in GRA (mean)
Pre-treatment socio-demographic characteristics
Age in years (mean)
Monthly net household income in Euros (mean)
Educational level (mean)
1-6
Years of unemployment (mean)
Local unemployment rate (mean)
Men (share)
Child in household (share)
Married (share)
Migration background (share)
East Germany (share)
Parallel life events (shares)
New job
Divorce
Separation
Death of spouse
Marriage
Child birth
Move (change of flat/house)
Pre-treatment job characteristics
Monthly net wage in Euros (mean)
Tenure in years (mean)
Level of occupational autonomy (mean)
1-5
Company size (mean)
1-3
Weekly working hours (mean)
Part time contract (share)
Public sector (share)
Sector of industry (shares, sum = 1.00)
Extraction, exploitation
Production
Construction
Trade and transport
Services
Media, finance, real estate
Administration, education, health
Source. SOEP 2004-2013.
11
Treatment group
239
mean/ standard
share deviation
Control group
37,461
mean/ standard
share deviation
4.77
-0.39
2.19
2.22
4.64
-0.09
2.13
2.19
0.356
0.034
44.92
1,612
3.64
0.54
0.09
0.62
0.37
0.68
0.15
0.26
9.49
686.63
1.29
1.17
0.04
43.73
1,843
4.04
0.43
0.09
0.56
0.35
0.64
0.09
0.24
9.67
979.99
1.44
1.12
0.04
0.747
0.000
0.000
0.132
0.310
0.079
0.646
0.274
0.010
0.441
0.62
0.03
0.04
0.00
0.04
0.04
0.03
14.51
12.18
2.60
2.12
41.48
0.16
0.07
0.02
0.32
0.08
0.08
0.27
0.14
0.10
0.10
0.01
0.03
0.00
0.04
0.03
0.03
7.33
9.64
1.00
0.83
9.02
16.32
12.93
2.94
2.33
40.86
0.18
0.32
0.04
0.26
0.05
0.06
0.11
0.14
0.35
Difference
test
p-value
0.000
0.135
0.496
0.681
0.622
0.681
0.919
8.38
10.03
1.06
0.77
9.54
0.000
0.229
0.000
0.000
0.285
0.285
0.000
0.006
0.039
0.096
0.226
0.000
0.921
0.000
5.2 The effect of job loss on the willingness to take risks
In the following, we present OLS estimations of our empirical model (1). The corresponding
results are presented in Table 2. We find a significantly negative effect of experiencing job loss
due to plant closure on GRA when controlling for the year of the interview of t = 0 only
(Column 2.1). We can thus conclude that the treatment effect does not originate from time
trends in risk aversion. Improving the comparability of treatment group and control group by
adding controls for pre-treatment socio-demographic characteristics only marginally affects the
size of the job loss coefficient (Column 2.2). The same applies to enlarging the model by further
controls for parallel life events accompanying job loss (Column 2.3) as well as pre-treatment
job characteristics (Column 2.4). In sum, the differences in pre-treatment characteristics
described in the previous section seem rather unimportant for the identification of the average
treatment effect. Altogether, the results presented in Table 2 strongly support our first
hypothesis, suggesting that job loss reduces GRA, i.e. increases absolute risk aversion. Beyond
the purpose of our study, we find that another life event, separation, increases willingness to
take risks.
Subjective survey items like GRA may undergo a structural change when the event of
interest affects the general answering behaviour of survey participants. Hence, the effect does
not necessarily need to reflect an actual change in the willingness to take risk, but rather results
from a change in the participant’s mood. To test this notion, we add additional subjective
covariates to model (1) that may be affected by mood effects, but are unrelated to our dependent
variable, namely the individual change in worries about environmental protection, maintaining
peace and crime in Germany. As none of the listed items changes the estimation results, we do
not find evidence for a structural survey bias.
12
Table 2: OLS estimation of the effect of job loss on risk tolerance
(2.1)
Job loss between t = −1 and t = 0
(2.2)
-0.292** (0.142)
Pre-treatment socio-demographics
Age in years
Monthly HH income (log)
ISCED Level (ref. level 4)
Level 1
Level 2
Level 3
Level 5
Level 6
Years of unemployment
Local unemployment rate (%)
Men
Child in HH
Married
Migration background
East Germany
(2.3)
(2.4)
-0.311**
(0.142)
-0.328**
(0.145)
-0.326** (0.145)
0.001
-0.025
(0.001)
(0.030)
0.002
-0.026
(0.001)
(0.030)
0.002
-0.002
(0.002)
(0.034)
0.272
0.128**
0.068
0.047
0.018
-0.011
-0.004
0.041*
0.060**
0.037
0.097**
0.049
(0.191)
(0.057)
(0.041)
(0.051)
(0.043)
(0.011)
(0.004)
(0.022)
(0.026)
(0.027)
(0.047)
(0.039)
0.275
0.130**
0.070*
0.046
0.019
-0.012
-0.004
0.043*
0.060**
0.038
0.099**
0.051
(0.191)
(0.057)
(0.041)
(0.051)
(0.043)
(0.011)
(0.004)
(0.023)
(0.026)
(0.028)
(0.047)
(0.039)
0.248
0.104*
0.057
0.043
0.034
-0.011
-0.004
0.056**
0.065**
0.034
0.083*
0.051
(0.193)
(0.059)
(0.042)
(0.052)
(0.046)
(0.011)
(0.004)
(0.028)
(0.028)
(0.028)
(0.048)
(0.040)
(0.037)
(0.095)
(0.061)
(0.287)
(0.059)
(0.065)
(0.090)
0.029
-0.122
0.226***
0.100
0.005
0.004
-0.036
(0.037)
(0.095)
(0.061)
(0.287)
(0.059)
(0.065)
(0.090)
Parallel life events
New job
Divorce
Separation
Death of spouse
Marriage
Child birth
Move (change of flat/house)
0.018
-0.124
0.224***
0.098
0.006
0.004
-0.041
Pre-treatment job characteristics
Gross hourly wage (Euros)
Tenure in years
Level of occ. autonomy (ref. level 3)
Level 1
Level 2
Level 4
Level 5
Company size up to 20 Emp.
Company size more than 200 Emp.
Weekly working hours
Part-time contract
Public sector
Sector of industry (ref. services)
Extraction, Exploitation
Production
Construction
Trade, transport
Media, finance, real estate
Administration, education, health
Year dummies
Constant
Observations
Adjusted R²
-0.001 (0.002)
0.001 (0.001)
yes
0.355***
yes
(0.029)
37,700
0.053
0.215***
yes
(0.051)
37,700
0.054
0.203***
0.034
0.010
-0.015
-0.027
-0.033
0.000
0.004
-0.004**
-0.049
(0.050)
(0.033)
(0.033)
(0.056)
(0.034)
(0.000)
(0.026)
(0.002)
(0.042)
0.004
0.007
0.009
0.009
-0.040
-0.053
(0.034)
(0.068)
(0.044)
(0.063)
(0.059)
(0.047)
yes
(0.052)
37,700
0.055
0.219***
(0.066)
37,700
0.055
Source. SOEP 2004-2013.
Note. The table presents OLS estimates of the change in GRA between t = −2 and t = 0. *** p<0.01, ** p<0.05,
* p<0.1, Robust standard errors in parentheses. The reference period is 2012, the reference group are employed
not experiencing an involuntary job loss with average age, tenure, years in unemployment, local unemployment
rate, hourly gross wage, level of autonomy and weekly working hours as well as ISCED level 4. Household (HH)
income weighted by OECD equivalent weights.
13
We repeat estimating the model with all of the controls (1) for age and gender subgroups
separately in order to test whether some of those amplify our results in particular (Table 3). As
the initial sample, all subgroups show a negative sign of the treatment effect. While age groups
hardy vary in the size of the effect, the gender gap is larger, suggesting men respond somewhat
stronger to job loss than women. However, job loss and gender interaction effects in an
estimation with the whole sample do not imply statistical significance for this gap.
Table 3. Subgroup results for the effect of job loss on risk tolerance
(2.4) the whole sample
OLS estimate of
job loss between t = −1 and t = 0
– 0.326** (0.145)
(3.1) age ≤ 44 years
– 0.353* (0.198)
(3.2) age ≥ 45 years
– 0.261 (0.210)
(3.3) women only
– 0.187
(3.4) men only
– 0.421
(0.226)
**
(0.187)
Source. SOEP 2004-2013.
Note. *p<0.1, **p<0.05 ***p<0.01. Robust standard errors in parentheses. The
dependent variable is the change in general willingness to take risks. Controls are
specified as in Table 2, Column 2.4. 44 is the sample median in age. Complete
results are presented in the Appendix, Table A1.
Further analyses reveal the importance of limiting the treatment group to workers who have lost
their jobs for the reason of plant closure. Including any dismissal by employer substantially
increases the coefficient of job loss on the willingness to take risks compared to Table 2.
Depending on the respective specification of (1), the β-coefficient for the broadly defined
treatment group is either slightly above or below zero, but always statistically insignificant. In
line with our theoretical considerations, losing work may not take people by surprise who are
dismissed for personal reasons and they may not lose much income in the wake of the event as
they are more likely to receive low earnings. This can in principle explain why our results differ
from those of Sahm (2012).
5.3 Anticipation and reversion
As companies get into trouble before they close (plants), workers will perceive an increase in
the risk of job loss and start to adjust absolute risk aversion before the actual closure
(Hypothesis 2). We therefore expect decreasing willingness to take risks between t = −2 and
t = −1 with the treated. To test this notion, we redefine the dependent variable as the change in
GRA between t = −2 and t = −1 and estimate the full model (1) again. Similarly, we test the
assumption of no anticipation of job loss before t = −2 by repeating our estimation of the full
14
model for the change in GRA between t = −3 and t = −2. Figure 1 displays the treatment effects
of these analyses, including the previous estimate of the impact of job loss in the change in
GRA from t = −2 to t = 0 as a yardstick. In line with Hypothesis 2, increasing uncertainty on
the eve of job loss decreases the willingness to take risks from t = −2 to t = −1 (p = 0.067). In
contrast, we do not observe a significant and substantial effect of future job loss on the change
in GRA from t = −3 to t = −2, suggesting that the risk attitude of treatment and control group
both follow a common trend before the event.
As described in Section 1, life events like very rare disasters change the perception of the
respective risk and thus increase risk aversion permanently. To test whether this applies to job
loss as well, we estimate the model (1) again for the change in GRA between t = −2 and the
second interview after the event has taken place, t = 1 (i.e. approximately 1 to 2 years after job
loss). This produces no significant effect of job loss, suggesting that the increase in risk aversion
until t = 0 is completely reversed afterwards. Experiencing job loss does not seem to increase
its perception in the future.
.25
0
-.25
-.5
Coefficient β:
job loss between t = -1 and t = 0
.5
Figure 1. Anticipation and reversion effects of job loss on risk tolerance
Average Marginal Effects of jobloss with 90% CIs
t = -3 to t = -2
t = -2 to t = -1
t = -2 to t = 0
t = -2 to t = 1
Source. SOEP 2004-2013.
Note. The figure illustrates the timeline of treatment effects obtained by running separate
estimations of changes in GRA from different reference points in time between t = −3 and t = 1
to t = −2. Droplines denote effect sizes. Whiskers denote 90% confidence intervals. Note that the
β-coefficients are predicted values based on the model specification presented in Column 2.4 of
Table 2 (full set of controls). Complete results of the underlying estimations are presented in
Table A2 in the Appendix.
15
5.4 High-skilled versus low-skilled workers
Throughout the whole analysis, we have assumed that the loss of current income and shattered
future income expectations are the main reasons why job loss may alter risk-taking. As a check
in this direction, Hypothesis 4 predicts that high-skilled workers (h-type) may respond more
strongly to an exogenous job loss than low-skilled workers (l-type). We therefore return to the
estimation of the change of GRA between t = −2 and t = 0 as dependent variable and estimate
separately the effects of interactions of the job loss variable with indicators of individual
productivity and proxies for skills.
Interacting job loss and pre-treatment gross hourly wage, as introduced by (2), yields the
strongest support for Hypothesis 4 (Column 4.1 of Table 4). At a hypothetical wage of zero
euros, losing work increases ∆GRA though not significantly. Each additional euro earned per
hour before job loss changes the effect of job loss on GRA significantly by −0.036 points.
Further checks reveal that the linear specification of this relationship seems reasonable. Thus,
job loss reduces GRA by −0.271 points (−0.325 points) at the median (mean) wage of 12.99
euros (14.51 euros).
Similarly, the GRAs of highly educated workers and workers with high pre-treatment
occupational autonomy (as further h-type proxies) respond somewhat more negatively to job
loss than the GRAs of the respective l-types (Columns 4.2 and 4.3). However, these differences
are not statistically significant (Wald-test for linear combination of β1 − (β2 + β3) yield
p = 0.529 (education) and p = 0.459 (autonomy)). These possible differences might reflect the
role of income again and point to non-wage characteristics like status and identity (education
and autonomy as proxies for occupational position).
16
Table 4. Estimation by level of skill
(4.1)
by hourly
wage
Job loss
(4.2)
by
education
(4.3)
by level of
autonomy
-0.388*
-0.416***
(0.207)
(0.161)
-0.252
-0.214
(0.189)
(0.235)
0.197
(0.321)
Gross hourly wage
-0.001
(0.002)
Job loss × gross hourly wage
-0.036**
(0.016)
Job loss × h-type (β1)
Job loss × l-type (β2)
l-type (β3)
0.036
0.017
(0.027)
(0.030)
yes
yes
yes
Constant
0.266***
0.241***
0.214***
(0.082)
(0.060)
(0.066)
Observations
Adjusted R²
37,700
0.055
37,700
0.055
37,700
0.055
Controls: year dummies, socio-demographics,
parallel shocks, job characteristics
Source. SOEP 2004-2013.
Note. *p<0.1, **p<0.05, ***p<0.01. Robust standard errors in parentheses. Hourly wage
calculated by gross monthly labour income and actual weekly working hours (in Euros).
Complete results of the underlying estimations are presented in Table A3 in the Appendix.
5.5 Employment status after job loss and the behavioural validity of stated risk attitude
Workers’ behaviour after job loss allows us to discuss the behavioural validity of our findings
on the stated willingness. Those individuals who change GRA most in response to job loss
should also be interested in reducing the damage as soon as possible by finding a new job.
Workers’ who are observed as reemployed at some early point in time after job loss may thus
have reduced willingness to take risks in particular. One might object that having found a job
balances the negative consequences of job loss, which is why reemployed people might have
readjusted their willingness to take risks, but this may be much more relevant in the long-run
than in the short-run (see also Section 5.3). At the beginning of a new employment spell,
workers have to survive probation (up to 0.5 years in Germany), are employed on a fixed-term
basis only, implying that their future employment stability and incomes are still at risk.
The time interval between job loss and the next SOEP interview is six months on average
in our sample. This should be sufficient for most workers to get at least one offer to take up a
new employment. In fact, the majority of workers have even started a new regular job in the
meantime (133 treated observations), whereas smaller groups are still registered as unemployed
17
(66) or do anything else (40, e.g. marginal employment). Estimating interaction effects of job
loss and these three states based on model (1) reveals that the reemployed have reduced their
willingness to take risks significantly more than workers who are still unemployed. As this
result also holds for subgroups of relatively low educated workers and relatively low wage
earners (as measured before job loss), it does not seem to reflect the high-skilled-low-skilled
difference of the previous Section 5.4 again. It rather points to a self-selection of workers into
reemployment who have reduced their willingness to take risks particularly.
The smaller group of unemployed workers who have not accepted a job offer six months
after job loss does not show any negative effect of job loss on GRA (the interaction effect of
job loss and being unemployed is positive in all model specifications, but statistically
insignificant). This points to a selection of workers into lasting unemployment who are not
described accurately by the assumptions we have made in Section 2. For instance, if there exists
a small group of workers with increasing absolute risk aversion in income (IARA), they will
not reduce their willingness to take risks in response to job loss and are hence ready to stay
unemployed for a longer time than the average worker while waiting for a good job match.
As a further check of the behavioural validity of our results, we calculate the impact of the
individual change in GRA between t = −2 and t = 0 on the job search duration of the treated.
A parametric survival time regression model is estimated using a Weibull distribution with the
start of regular part-time or fulltime employment as exit event of interest. Individuals who retire,
take part in training schemes or are marginally employed after job loss are excluded from the
analysis, because it is not clear whether they actually search for a regular job. Additionally, we
do not consider observations of workers who do not report an exact start date of their new
employment or report more than 60 months of unemployment. Altogether, we obtain 187 spells
out of our initial sample of 239. 154 report the exit event. To control for demand effects on the
probability of job finding after job loss, we control for education (ISCED level) and pretreatment (t = −2) gross hourly wage. In addition, we include gender as explaining variable.
Men might feel pushed more to search for a new job because of their breadwinner identity. As
shown in Table A4, ∆GRA between t = −2 and t = 0 is negatively related to the probability of
job finding, i.e. the more it reduces in response to job loss the quicker people are observed as
reemployed. Figure 2 illustrates the predicted survival rates when GRA changes by 0, −1 or −2
after job loss. A reduction in GRA by one (two) point(s) reduces the expected time in
18
unemployment by approximately half a month (one month) compared to a zero change. 6 In sum,
we find evidence in support of the view that those workers who adjust risk attitude in particular
in response to job loss try to reduce the associated damage as soon as possible by searching
intensely for a new job.
1
Figure 2. Survival rates depending on the individual change in GRA
.6
.4
0
.2
Survival rate
.8
∆GRA = 0
∆GRA = -1
∆GRA = -2
0
2
4
6
8
10
12
14
16
18
20
Exposure time in unemployment (in months)
Source. SOEP 2004-2013.
Note. Predicted survival rates for ∆GRA = 0, ∆GRA = −1 and ∆GRA = −2, based on the
model specification displayed in Column (4.3) of Table A4.
5.6 Robustness checks
In this section, we analyse possible threats to the validity of our identifying assumption,
according to which job losses triggered by plant closures hit workers exogenously. A first issue
to consider is that of small firms. When the failing business employs a very few people only,
the single employee can influence the firms’ survival. We therefore run our estimations again
based on samples excluding employees of small firms stepwise (below five / below ten / below
twenty employees). Compared to the initial estimations presented up to here, the effects of job
loss on the willingness to take risks slightly increases in size (while staying statistically
significant) the more we exclude small firms. Thus, the more the exogeneity assumption is
reasonable, the stronger are our results.
6
As we find anticipation on the eve of job loss, people may start searching for a new job before the event ultimately
occurs. A negative anticipation effect (∆GRA from t = −2 to t = −1) should thus expedite reemployment. In fact,
repeating our analysis of job finding for ∆GRA from t = −2 to t = −1 as explaining variable yields this result.
19
If related to risk attitudes, self-selection out of the firm before job loss might take place as
we have seen that people may often anticipate plant closure from a certain point in time onwards
(Section 5.3). It is a priori not clear into which direction such a selection leads. Assume workers
realise at some point in time that the probability of a plant closure increases. People who
respond by leaving the firm take the risks of immediate unemployment and job search as well
as the risk of uncertain characteristics associated with a potential new job and might thus be
relatively risk-prone. However, workers who stay in a firm that is on the rocks take the
increasing risk of future unemployment and job search, which could also reflect high
willingness to take risks. These two forces work in opposite directions, which could explain
why pre-treatment GRA does not vary significantly between treatment and control group (Table
1 in Section 5.1). To analyse this insight further, we estimate the probability to experience a job
loss triggered by plant closure starting from t = −2 in about one to two years conditioned on the
willingness to take risks in t = −2. As some of the individual characteristics documented in
Table 1 differ on average between treatment and control group, we also consider the full set of
socio-demographics and job characteristics in this part of the analysis. Columns A5.1 and A5.2
in Table A5 in the Appendix document the corresponding probit estimation results. They hardly
point to any selection into plant closure. Neither the level of the willingness to take risks nor
other variables explains the probability of experiencing this reason for job loss in the near
future, except some of the sector of industry dummies.
Applying the same probit estimations to the probability of experiencing any dismissal other
than plant closure in the next two years supports the conclusion from the regression results that
those terminations of employment are rather endogenous, which is in line with the results of
Sahm (2012). Workers are the more likely to be dismissed the higher their willingness to take
risks, age and number of years they spent unemployed in the past as well as the lower their level
of education (Columns A5.3 and A5.5 in Table A5). In contrast, tenure, working hours, overall
income and labour earnings decrease the probability of being dismissed in the near future. As
many observables are related to other types of dismissal, selection on unobservables may also
be a big issue here. It is hence a reasonable strategy to focus on plant closures only, although
the number of observations is not very high.
20
5.7 Economic significance of the job loss effect on the willingness to take risks
To further assess the economic meaningfulness of our findings, we compare the marginal effect
of GRA on different economic decisions with the change in GRA caused by loss of job. In so
doing, we can clarify whether the results potentially translate into a meaningful change in
behaviour under uncertainty. Table 5 lists marginal effects of GRA on different economic
decisions which have been identified in the literature. Hereinafter, a male worker who has
adjusted GRA by −0.421 in response to job loss according to Table 3 serves as an example for
our results.
According to Bonin et al. (2007) an increase in GRA of one point increases the monthly
earning by 1.3%. Therefore, in case of a job loss the reduction in GRA of −0.421 for men could
resolve in a reduction in monthly wages by 0.5% (−0.421 × 0.013). The marginal effect of GRA
for the probability of being public sector employed identified by Pfeifer (2010) is −0.8% by a
given average probability of 32.0%, a job loss would translate in an increasing probability to
work in the public sector by 0.33%-points (−0.421 × −0.008). A bigger effect can be expected
for everyday decisions examined by Dohmen et al. (2011). A job loss could reduce the average
probability of investments in stocks by 3.5% (−0.421 × 0.029 / 0.341), of doing sports by 3.9%
and of smoking by 5.3%. Caliendo et al. (2014) estimate an average probability to enter selfemployment within one year of 1.1%. This transition probability decreases by 0.02%-points
when GRA reduces by one point. Straightforward, when a job loss reduces the GRA of men by
−0.421, the probability to start one one’s business decreases by 0.008%-points which equals a
relative drop in probability of 0.7%. Another effect can be expected with respect to migration.
Jaeger et al. (2010) estimate a marginal effect of GRA on the decision to migrate within the
next five years by 0.26%-points for a given average probability of 5.8%. A job loss thus causes
a reduction of the migration probability for males by 0.0026 × −0.421 = −0.1%-points or by 2%
of the average probability to migrate in the next five years.
21
Table 5. Marginal effects of GRA in the literature
Average
probability
(in percent)
Study
Dependent variable
Bonin et al. (2007)
Monthly earnings
Jaeger et al. (2010)
Migration within next five years
Pfeifer (2010)
Marginal
Effect
(in percentage points)
1.3
5.8
0.3
Being public sector employed
32.0
-0.8
Dohmen et al. (2011)
Investment in stocks
Doing active sports
Smoking
34.1
66.2
29.4
2.9
6.1
3.7
Caliendo et al. (2014)
Enter self-employment within next year
1.1
0.02
Note. As GRA is included in a non-linear manner in Caliendo et al. (2014), marginal effect is given for
GRA equal to 5. All studies estimate a binary choice model, except Bonin et al. (2007). Marginal effect in
Bonin et al. (2007) is a semi-elasticity and given in percent.
6.
Conclusions
The principle possibility of job loss produces the most important income risk to most workers.
Our results show that an increase in this risk reduces the willingness to take other risks. They
thus correspond to recent findings of the research on similar background risks like disasters that
imply an analogous change in risk aversion. However, the impacts of a rare disaster and the
more common loss of employment may have different origins. Since we find the risk-taking
effect of job loss to be of a transitory nature only, it does not seem to come from a general
change in the individual perception of this specific risk, as it has been discussed with respect to
natural disasters (e.g. Cameron and Shah 2015). Instead, we find strong evidence that the
income shock associated with job loss changes risk attitude for some time.
Our findings do not match those of the study that is related most closely to ours and does
not reveal an effect of job loss on risk-taking (Sahm 2012). In principle, this might originate
from the different countries analysed or the different direct measures of risk attitude applied,
but we suspect the composition of the treatment groups is crucial here. We would also be not
able to measure a significant effect if our treated included all dismissed workers like that of
Sahm (2012), instead of being limited to losses of work for the reason of plant closure. People
who are dismissed for personal reasons might have been able to affect the risk of job loss and
are thus not surprised in the case that job loss occurs. As a consequence, they do not modify
risk attitude.
22
The GRA as a directly measured risk attitude responds clearly to the negative shock of loss
of work. Whether this maps into decision-making in various contexts cannot be documented
per se, because behavioural changes can be hampered by inertia (Brunnermeier and Nagel 2008)
or other dominant factors. At least on the labour market, however, our results are reflected in
workers’ decisions as they accept job offers according to the change job loss has caused in their
risk attitude. The stronger workers’ willingness to take risks responds to the event, the quicker
they are observed as reemployed, probably to the end of reducing the income loss associated
with job loss.
The fact that the GRA measure we use responds to job loss has two methodological
implications. Firstly, research on the impact of risk attitude on any outcome cannot assume this
measure to be exogenously given like a stable personality trait. Reverse causality and third
variable bias can in principle concern such analyses. Secondly, the overall pattern we document
is very consistent with the view that the GRA measures absolute risk aversion or, in other words,
a local risk preference. GRA changes as background risk in the utility function is altered by the
(forthcoming) calamity and it returns to its initial level as the consequences of job loss are
removed. In the absence of contrary theoretical foundations of the GRA, this speaks in favour
of using the survey item as inverse measure of ARA. It also implies that our findings should
not be misinterpreted as evidence for unstable general risk preferences, which are reflected by
the shape of the utility function. Quite the contrary, they are well compatible with expected
utility reasoning on the impact of job loss on the willingness to take risks.
23
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26
Appendix
Table A1. OLS estimations of subgroup analysis
Subgroup
Job loss between t = -1 and t = 0
Age ≤ 44
Age > 44
Women only
Men only
-0.353*
(0.198)
-0.261
(0.210)
-0.187
(0.226)
-0.421**
(0.187)
0.001
-0.029
(0.002)
0.002
0.013
(0.002)
0.325
0.142
0.074
0.084
0.047
-0.018
-0.000
(0.332)
0.213
0.082
0.040
0.016
0.023
0.001
-0.007
(0.238)
0.058
0.016
0.084
0.032
(0.043)
(0.041)
(0.073)
0.071*
0.057
0.081
0.069
(0.037)
(0.039)
(0.064)
(0.059)
(0.048)
(0.357)
(0.090)
(0.268)
(0.142)
0.043
0.086
0.301***
0.071
0.067
0.025
0.039
Pre-treatment socio-demographics
Age in years
Monthly HH income (log)
ISCED Level (ref. level 4)
Level 1
Level 2
Level 3
Level 5
Level 6
Years of unemployment
Local unemployment rate (%)
Men
Child in HH
Married
Migration background
East Germany
0.011
(0.051)
-0.012
(0.046)
0.245
0.087
0.074
0.041
0.041
-0.016
-0.006
0.012
0.081**
0.079**
0.060
0.057
(0.277)
0.262
0.123
0.032
0.035
0.014
-0.010
-0.002
0.098**
0.056
-0.022
0.100
0.053
(0.267)
Parallel life events
New job
Divorce
Separation
Death of spouse
Marriage
Child birth
Move
0.046
-0.188
0.349***
0.027
0.049
0.018
-0.080
(0.044)
(0.069)
(1.159)
(0.067)
(0.066)
(0.099)
-0.004
-0.038
-0.099
0.121
-0.101
-0.360
0.155
(0.269)
(0.126)
(0.399)
(0.207)
0.019
-0.365***
0.156*
0.123
-0.081
-0.274
-0.134
0.002
-0.002
(0.003)
(0.003)
-0.003
0.001
(0.002)
(0.002)
0.004
0.001
(0.003)
(0.002)
-0.003
0.001
(0.002)
(0.002)
0.096
0.043
-0.038
0.025
-0.027
-0.006**
-0.146**
0.059
-0.027
(0.071)
-0.029
-0.029
0.004
-0.044
0.029
-0.002
0.032
-0.042
0.029
(0.069)
-0.022
-0.036
-0.050
-0.052
0.002
0.038
-0.005*
-0.053
-0.008
(0.080)
0.085
0.063
0.024
0.010
-0.067
-0.020
-0.003
-0.061
0.010
(0.065)
-0.020
-0.006
-0.006
-0.041
-0.085
-0.059
(0.093)
(0.100)
(0.064)
-0.005
-0.015
0.009
-0.055
-0.047
-0.007
(0.084)
(0.072)
0.011
0.041
-0.107
-0.044
-0.078
-0.016
(0.151)
(0.064)
0.046
0.038
0.036
-0.026
-0.002
0.055
(0.087)
0.246**
(0.113)
0.259***
(0.094)
0.243***
Pre-treatment job characteristics
Gross hourly wage (Euros)
Tenure in years
Level of occ. autonomy (ref. level 3)
Level 1
Level 2
Level 4
Level 5
Company size up to 20 Emp.
Company size more than 200 Emp.
Weekly working hours
Part time contract
Public sector
Sector of industry (ref. Services)
Extraction, Exploitation
Production
Construction
Trade, transport
Media, finance, real estate
Administ., education, health
Year dummies
Constant
Observations
R-squared
(0.079)
(0.052)
(0.067)
(0.059)
(0.018)
(0.006)
(0.039)
(0.041)
(0.039)
(0.066)
(0.058)
(0.123)
(0.071)
(0.044)
(0.047)
(0.088)
(0.037)
(0.002)
(0.061)
(0.048)
(0.037)
(0.058)
(0.084)
(0.082)
(0.063)
yes
0.212**
(0.093)
(0.075)
(0.086)
(0.077)
(0.015)
(0.006)
(0.041)
(0.042)
(0.039)
(0.070)
(0.055)
(0.149)
(0.121)
(0.049)
(0.046)
(0.073)
(0.037)
(0.002)
(0.057)
(0.048)
(0.037)
(0.066)
(0.095)
(0.087)
(0.073)
yes
18,940
0.051
(0.051)
(0.089)
(0.059)
(0.077)
(0.066)
(0.016)
(0.007)
(0.059)
(0.133)
(0.087)
(0.049)
(0.052)
(0.094)
(0.049)
(0.040)
(0.003)
(0.054)
(0.049)
(0.067)
(0.119)
(0.103)
(0.068)
yes
18,760
0.062
16,602
0.056
(0.048)
(0.079)
(0.061)
(0.072)
(0.064)
(0.016)
(0.006)
(0.054)
(0.134)
(0.085)
(0.475)
(0.077)
(0.067)
(0.112)
(0.045)
(0.043)
(0.071)
(0.047)
(0.035)
(0.002)
(0.095)
(0.048)
(0.062)
(0.078)
(0.078)
(0.068)
(0.073)
yes
(0.094)
21,098
0.055
Source. SOEP 2004-2013.
Note. The table presents OLS estimates of the change in GRA for certain subgroups indicated by the first column.
***
p<0.01, ** p<0.05, * p<0.1, robust standard errors in parentheses. The reference group exhibits the average
age, tenure, actual working hours, net labour/household income, and local unemployment rate. It is female, is not
living with children in the same household, is not married, its ISCED level of education is 4 and is fulltime
employed. Household (HH) income weighted by OECD equivalent weights.
27
Table A2. OLS estimations of anticipation and reversion of a job loss on GRA
ΔGRA between
Job loss between t = -1 and t = 0
Pre-treatment socio-demographics
Age in years
Monthly HH income (log)
ISCED Level (ref. level 4)
Level 1
Level 2
Level 3
Level 5
Level 6
Years of unemployment
Local unemployment rate (%)
Men
Child in HH
Married
Migration background
East Germany
Parallel life events
New job
Divorce
Separation
Death of spouse
Marriage
Child birth
Move
Pre-treatment job characteristics
Gross hourly wage (Euros)
Tenure in years
Level of occ. autonomy (ref. level 3)
Level 1
Level 2
Level 4
Level 5
Company size up to 20 Emp.
Company size more than 200 Emp.
Weekly working hours
Part time contract
Public sector
Sector of industry (ref. Services)
Extraction, Exploitation
Production
Construction
Trade, transport
Media, finance, real estate
Administ., education, health
t = −3 and t = −2
t = −2 and t = −1
t = −2 and t = 0
t = −2 and t = 1
0.065
(0.274)
-0.309*
(0.169)
-0.322**
(0.144)
0.024
(0.175)
0.003
-0.038
(0.003)
0.000
-0.011
(0.002)
0.002
-0.002
(0.002)
-0.000
0.064
(0.002)
0.000
0.049
0.036
0.011
0.039
-0.017
-0.001
-0.001
-0.001
-0.011
0.082
-0.010
(0.394)
-0.025
0.079
0.020
0.006
0.018
-0.009
-0.010*
0.015
0.026
0.001
-0.012
0.060
(0.269)
0.248
0.104*
0.057
0.043
0.034
-0.011
-0.004
0.056**
0.065**
0.034
0.083*
0.051
(0.193)
0.148
-0.013
-0.007
-0.023
-0.077
0.014
-0.007
0.090**
0.074**
0.038
-0.004
0.096*
(0.252)
0.017
-0.277*
0.232**
0.401
0.031
0.004
-0.355**
(0.060)
(0.156)
(0.044)
(0.111)
(0.090)
0.027
-0.156
0.108
0.183
0.085
0.184**
-0.183*
(0.049)
(0.116)
(0.104)
0.029
-0.122
0.226***
0.100
0.005
0.004
-0.036
(0.037)
(0.095)
(0.161)
0.035
-0.120
0.169**
-0.037
0.001
0.041
0.047
0.000
0.000
(0.003)
(0.002)
-0.003
0.001
(0.002)
(0.002)
-0.001
0.001
(0.002)
(0.001)
-0.006**
0.002
(0.002)
(0.002)
-0.017
0.014
0.039
0.067
-0.047
0.016
-0.003
-0.100
-0.025
(0.084)
0.023
-0.003
-0.006
0.048
-0.035
-0.001
-0.005**
-0.101**
0.014
(0.059)
0.034
0.010
-0.015
-0.027
-0.033
0.004
-0.004**
-0.049
0.004
(0.050)
0.054
-0.115***
-0.015
-0.071
0.009
0.044
-0.003
-0.045
0.043
(0.063)
0.015
0.032
0.006
0.071
0.113
0.077
(0.105)
0.022
0.020
0.026
0.012
-0.019
0.002
(0.079)
0.007
0.009
0.009
-0.040
-0.053
-0.005
(0.068)
0.114
0.090
0.140*
0.031
-0.078
0.008
(0.086)
(0.055)
(0.098)
(0.067)
(0.083)
(0.073)
(0.018)
(0.008)
(0.045)
(0.044)
(0.044)
(0.078)
(0.060)
(0.099)
(0.422)
(0.101)
(0.118)
(0.052)
(0.053)
(0.092)
(0.055)
(0.043)
(0.003)
(0.066)
(0.055)
(0.072)
(0.097)
(0.098)
(0.077)
(0.077)
Year dummies
yes
Constant
Observations
R-squared
-0.557*** (0.102)
13,702
0.048
yes
(0.040)
(0.070)
(0.049)
(0.060)
(0.053)
(0.014)
(0.006)
(0.033)
(0.032)
(0.032)
(0.057)
(0.045)
(0.072)
(0.304)
(0.069)
(0.079)
(0.038)
(0.038)
(0.064)
(0.040)
(0.031)
(0.002)
(0.048)
(0.040)
(0.051)
(0.073)
(0.071)
(0.055)
(0.055)
yes
(0.034)
(0.059)
(0.042)
(0.052)
(0.046)
(0.011)
(0.004)
(0.028)
(0.028)
(0.028)
(0.048)
(0.040)
(0.061)
(0.287)
(0.059)
(0.065)
(0.033)
(0.033)
(0.056)
(0.034)
(0.026)
(0.002)
(0.042)
(0.034)
(0.044)
(0.063)
(0.059)
(0.047)
(0.048)
(0.044)
(0.076)
(0.053)
(0.064)
(0.057)
(0.015)
(0.006)
(0.036)
(0.036)
(0.035)
(0.062)
(0.050)
(0.080)
(0.364)
(0.077)
(0.084)
(0.111)
(0.041)
(0.041)
(0.070)
(0.043)
(0.033)
(0.002)
(0.053)
(0.044)
(0.055)
(0.079)
(0.076)
(0.059)
(0.060)
yes
0.123
(0.076) 0.219***
(0.066) -0.193**
(0.083)
25,602
37,700
23,457
0.038
0.055
0.083
Source. SOEP 2004-2013.
Note. The table presents OLS estimates of the change in GRA between the period indicated by the first column.
***
p<0.01, ** p<0.05, * p<0.1, robust standard errors in parentheses. The reference group exhibits the average
age, tenure, actual working hours, net labour/household income, and local unemployment rate. It is female, is
not living with children in the same household, is not married, its ISCED level of education is 4 and is fulltime
employed. Household (HH) income weighted by OECD equivalent weights.
28
Table A3. OLS estimations by level of skill
By hourly wage
Job loss between t = -1 and t = 0
× Gross hourly wage (Euros)
× high skill level (binary)
× low skill level (binary)
No job loss × low skill level (binary)
0.197
-0.036**
By level of education
By level of autonomy
(0.321)
(0.016)
-0.388*
-0.252
0.036
(0.207)
(0.027)
−0.416***
−0.214
0.017
0.002
-0.002
(0.002)
(0.034)
0.002
-0.005
(0.002)
(0.034)
(0.192)
(0.058)
(0.042)
(0.052)
(0.044)
(0.011)
(0.004)
(0.028)
(0.027)
(0.028)
(0.048)
(0.040)
(0.189)
(0.161)
(0.235)
(0.030)
Pre-treatment socio-demographics
Age in years
Monthly HH income (log)
ISCED Level (ref. level 4)
Level 1
Level 2
Level 3
Level 5
Level 6
Years of unemployment
Local unemployment rate (%)
Men
Child in HH
Married
Migration background
East Germany
0.002
-0.002
(0.002)
(0.034)
0.248
0.104*
0.056
0.043
0.034
-0.011
-0.004
0.056**
0.065**
0.034
0.083*
0.051
(0.193)
(0.059)
(0.042)
(0.052)
(0.046)
(0.011)
(0.004)
(0.028)
(0.028)
(0.028)
(0.048)
(0.040)
-0.010
-0.004
0.058**
0.066**
0.034
0.089*
0.051
(0.011)
(0.004)
(0.028)
(0.027)
(0.028)
(0.048)
(0.039)
0.257
0.108*
0.057
0.041
0.028
-0.010
-0.004
0.054*
0.064**
0.034
0.086*
0.051
Parallel life events
New job
Divorce
Separation
Death of spouse
Marriage
Child birth
Move
0.030
-0.122
0.228***
0.099
0.004
0.003
-0.037
(0.037)
(0.095)
(0.061)
(0.287)
(0.059)
(0.065)
(0.090)
0.030
-0.122
0.226***
0.098
0.004
0.003
-0.037
(0.037)
(0.095)
(0.061)
(0.287)
(0.059)
(0.065)
(0.090)
0.030
-0.122
0.226***
0.100
0.005
0.004
-0.036
(0.037)
(0.095)
(0.061)
(0.287)
(0.059)
(0.065)
(0.090)
-0.001
0.001
(0.002)
(0.001)
-0.001
0.001
(0.002)
(0.001)
-0.001
0.001
(0.002)
(0.001)
0.033
0.009
-0.015
-0.028
-0.033
0.004
-0.004**
-0.050
0.003
(0.049)
(0.032)
(0.032)
(0.055)
(0.034)
(0.026)
(0.002)
(0.042)
(0.034)
0.047
0.009
-0.009
-0.023
-0.032
0.005
-0.004**
-0.050
0.006
(0.049)
(0.033)
(0.031)
(0.055)
(0.034)
(0.026)
(0.002)
(0.042)
(0.034)
-0.034
0.005
-0.004**
-0.051
0.002
(0.034)
(0.026)
(0.002)
(0.042)
(0.034)
Pre-treatment job characteristics
Gross hourly wage (Euros)
Tenure in years
Level of occ. autonomy (ref. level 3)
Level 1
Level 2
Level 4
Level 5
Company size up to 20 Emp.
Company size more than 200 Emp.
Weekly working hours
Part time contract
Public sector
Sector dummies
Year dummies
Constant
Observations
R-squared
yes
yes
yes
yes
0.266*** (0.082)
37,700
0.055
0.241***
(0.060)
37,700
0.055
yes
yes
0.214***
(0.066)
37,700
0.055
Source. SOEP 2004-2013.
Note. The table presents OLS estimates of the change in GRA. *** p<0.01, ** p<0.05, * p<0.1, robust standard
errors in parentheses. The reference group exhibits the average age, tenure, actual working hours, net
labour/household income, and local unemployment rate. It is female, is not living with children in the same
household, is not married, its ISCED level of education is 4 and is fulltime employed. Household (HH) income
weighted by OECD equivalent weights. Interaction terms between two variables are indicated by „×”.
29
Table A4. Estimation results of parametric survival time regression
(4.1)
∆GRA
Men
(4.2)
-0.120*** (0.041)
-0.109** (0.045)
-0.120***
(0.044)
0.262 (0.191)
0.207 (0.195)
0.270
(0.199)
-0.048
-0.790*
-0.307
0.531
0.484
(0.542)
(0.466)
(0.377)
(0.527)
(0.396)
-0.017
(0.012)
ISCED Level (ref. level 4)
Level 1
Level 2
Level 3
Level 5
Level 6
-0.004
-0.745
-0.288
0.435
0.393
(0.546)
(0.476)
(0.388)
(0.529)
(0.389)
Gross hourly wage
Log likelihood
Observations
(4.3)
-0.526
187
-0.570
187
-0.519
187
Source. SOEP 2004-2013.
Note. *p<0.1, **p<0.05, ***p<0.01. Robust standard errors in parentheses. Coefficients
reported, not hazard rates. 154 observations report an exit event. ISCED level and gross
hourly wage taken in t = −2. ∆GRA denotes change in GRA between t = −2 and t = 0 for
column (4.1), (4.2) and (4.3).
30
Table A5. The probabilities of future job loss by plant closure and by other kinds of dismissal
Dependent Variable
GRA in t = −2
(5.1)
(5.2)
Plant closure between t = −1 and t = 0
0.010
(0.011)
Pre-treatment socio-demographics
Age in years
Monthly HH income (log)
ISCED Level (ref. level 4)
Level 1
Level 2
Level 3
Level 5
Level 6
Years of unemployment
Local unemployment rate (%)
Men
Child in HH
Married
Migration background
East Germany
Job characteristics in t = −2
Gross hourly wage (Euros)
Tenure in years
Level of occ. autonomy (ref. level 3)
Level 1
Level 2
Level 4
Level 5
Company size up to 20 Emp.
Company size more than 200 Emp.
Weekly working hours
Part time contract
Public sector
Sector of industry (ref. Services)
Extraction, Exploitation
Production
Construction
Trade, transport
Media, finance, real estate
Administ., education, health
Year dummies
Constant
Observations
Pseudo R-squared
yes
-2.629***
0.007
(0.012)
0.005
-0.038
(0.003)
0.056
0.062
-0.035
-0.024
-0.076
-0.001
0.010
0.022
0.016
0.044
0.150*
0.056
(0.292)
-0.012
0.108
(0.089)
-0.041
0.096
0.014
-0.253
0.091
-0.031
0.000
-0.038
-0.374***
(0.093)
-0.554***
-0.235***
-0.207**
-0.153
-0.214**
-0.444**
(0.173)
37,700
0.004
-2.367***
0.025***
(0.007)
(0.075)
(0.120)
(0.093)
(0.121)
(0.109)
(0.021)
(0.010)
(0.063)
(0.059)
(0.059)
(0.078)
(0.292)
(0.106)
(0.065)
(0.080)
(0.182)
(0.065)
(0.059)
(0.004)
(0.094)
(0.100)
(0.072)
(0.104)
(0.102)
(0.085)
(0.100)
yes
(0.078)
(5.3)
(5.4)
Dismissed between t = −1 and t = 0
yes
(0.143)
37,700
0.057
0.032***
(0.008)
0.015***
-0.161***
(0.002)
0.349**
0.056
0.054
0.095
0.041
0.039***
0.006
0.047
-0.051
-0.075*
-0.127**
0.349**
(0.175)
-0.057
0.031
(0.061)
0.071
0.012
-0.029
0.033
0.167***
-0.132***
-0.023***
-0.323***
-0.365***
(0.058)
0.016
0.028
0.146**
0.045
0.064
-0.109*
(0.094)
(0.088)
(0.068)
(0.086)
(0.079)
(0.010)
(0.007)
(0.043)
(0.041)
(0.040)
(0.062)
(0.175)
(0.067)
(0.046)
(0.061)
(0.128)
(0.042)
(0.043)
(0.002)
(0.059)
(0.064)
(0.055)
(0.070)
(0.078)
(0.062)
(0.064)
yes
-2.065*** (0.044) -2.152***
38,188
0.006
(0.052)
(0.101)
38,188
0.153
Source. SOEP 2004-2013.
Note. *p<0.1, **p<0.05, ***p<0.01. Robust standard errors in parentheses. The reference group exhibits the average level
of willingness to take risks, age, tenure, actual working hours, net labour/household income, and local unemployment
rate. It is female, is not living with children in the same household, is not married, its ISCED level of education is 4 and
is fulltime employed.
31
Diskussionsbeiträge - Fachbereich Wirtschaftswissenschaft - Freie Universität Berlin
Discussion Paper - School of Business and Economics - Freie Universität Berlin
2015 erschienen:
2015/1
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The effects of increasing the standards of the high school curriculum on school
dropout
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BÖNKE, Timm und Clive WERDT
Charitable giving and its persistent and transitory reactions to changes in tax
incentives: evidence from the German Taxpayer Panel
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WERDT, Clive
What drives tax refund maximization from inter-temporal loss usage? Evidence from
the German Taxpayer Panel
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2015/4
FOSSEN, Frank M. und Johannes KÖNIG
Public health insurance and entry into self-employment
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2015/5
WERDT, Clive
The elasticity of taxable income for Germany and its sensitivity to the appropriate
model
Economics
2015/6
NIKODINOSKA, Dragana und Carsten SCHRÖDER
On the Emissions-Inequality Trade-off in Energy Taxation: Evidence on the German
Car Fuel Tax
Economics
2015/7
GROß, Marcus; Ulrich RENDTEL; Timo SCHMID; Sebastian SCHMON und Nikos
TZAVIDIS
Estimating the density of ethnic minorities and aged people in Berlin: Multivariate
kernel density estimation applied to sensitive geo-referenced administrative data
protected via measurement error
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SCHMID, Timo; Nikos TZAVIDIS; Ralf MÜNNICH und Ray CHAMBERS
Outlier robust small area estimation under spatial correlation
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2015/9
GÖRLITZ, Katja und Marcus TAMM
Parenthood and risk preferences
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2015/10
BÖNKE, Timm; Giacomo CORNEO und Christian WESTERMEIER
Erbschaft und Eigenleistung im Vermögen der Deutschen: eine Verteilungsanalyse
Economics
2015/11
GÖRLITZ, Katja und Marcus TAMM
The pecuniary and non-pecuinary returns to voucher-financed training
Economics
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CORNEO, Giacomo
Volkswirtschaftliche Bewertung öffentlicher Investitionen
Economics
2015/13
GÖRLITZ, Katja und Christina Gravert
The effects of a high school curriculum reform on university enrollment and the
choice of college major
Economics
2015/14
BÖNKE, Timm und Carsten SCHRÖDER
European-wide inequality in times of the financial crisis
Economics
2015/15
BÖNKE, Timm; Beate JOACHIMSEN und Carsten SCHRÖDER
Fiscal federalism and tax enforcement
Economics
2015/16
DEMMER, Matthias
Improving Profitability Forecasts with Information on Earnings Quality
FACTS
2015/17
HAAN, Peter und Victoria PROWSE
Optimal Social Assistance and Unemployment Insurance in a Life-cycle Model of
Family Labor Supply and Savings
Economics
2015/18
CORNEO, Giacomo, Carsten SCHRÖDER und Johannes KÖNIG
Distributional Effects of Subsidizing Retirement Savings Accounts: Evidence from
Germany
Economics
2015/19
BORGONI, Riccardo; Paola DEL BIANCO; Nicola SALVATI; Timo SCHMID und
Nikos TZAVIDIS
Modelling the distribution of health related quality of life of advanced melanoma
patients in a longitudinal multi-centre clinical trial using M-quantile random
effects regression
Economics
2015/20
HELLER, C.-Philipp; Johannes JOHNEN und Sebastian SCHMITZ
Congestion Pricing: A Mechanism Design Approach
Economics
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BARTELS, Charlotte und Nico PESTEL
The Impact of Short- and Long-term Participation Tax Rates on Labor Supply
Economics
2015/22
JESSEN, Robin; Davud ROSTAM-AFSCHAR und Viktor STEINER
Getting the Poor to Work: Three Welfare Increasing Reforms for a Busy Germany
Economics
2015/23
BLAUFUS, Kay; Matthias BRAUNE; Jochen HUNDSDOERFER und Martin JACOB
Does Legality Matter? : The Case of Tax Avoidance and Evasion
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2015/24
RENDTEL, Ulrich
Warum im Zensus die Ergebnisse der Stichprobenmethode keine Benachteiligung
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RENDTEL, Ulrich
Is there a fade-away effect of initial nonresponse bias in EU-SILC?
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2015/26
BÖNKE, Timm; Matthias GIESECKE und Holger LÜTHEN
The Dynamics of Earnings in Germany: Evidence from Social Security Records
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GROß, Marcus und Ulrich RENDTEL
Kernel Density Estimation for Heaped Data
Economics
2015/28
DWENGER, Nadja; Frank M. FOSSEN und Martin SIMMLER
From financial to real economic crisis: Evidence from individual firm-bank
relationships in Germany
Economics
2015/29
HACHULA, Michael und Sebastian HOFFMANN
The Output Effects of Commodity Price Volatility: Evidence from Exporting
Countries
Economics
2015/30
EICHFELDER, Sebastian; Frank HECHTNER und Jochen HUNDSDOERFER
Formula apportionment: Factor allocation and tax avoidance
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2015/31
WAGNER, Julia
EBITDA-Vortrag – cui bono? : Eine Gesetzesevaluation auf Basis einer
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